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- SPSS Processer
- Descriptive statistics
- Hypothesis Testing
- Testing the differences between group means
- Correlational Analysis
- Regression (Linear & Multiple)
- Logistic regression
- Non-parametric tests
- Factor Analysis
- Lab Work & Project
SPSS refers to statistical Package for the Social Science which is a software package for data management and statistical analysis. SPSS is mostly used in the field of Social Sciences and business. It uses graphical and syntactical interface and used to perform all of the analysis. SPSS can take the data from any type of files as it is capable of handling large amount of data.
Major features of SPSS are as follows:
- It comes with a lot of editing tools and data management system
- SPSS provides eminent reporting, presentation and plotting features.
- It is easy to use and learn
- SPSS provides in-depth statistical capabilities.
- It has a diverse range of tools for managing data.
SPSS is a widely used powerful program which offers many ways to examine data. it comes with several components which includes data collection tools, analyzing data for spot patterns and for make predictions, analytic catalyst for spot pattern in data without identify the goal. SPSS uses its two major techniques which are as follows:
- Regression Techniques: this technique involves a wide range of factors that may influence a specific set of data, eliminating all the effect and then recognizing patterns.
- Prediction Techniques: Prediction techniques is the another types of technique that is used by SPSS. This technique is used for the forecasting purpose which involves taking cause and effect figures and then applying them into a specific set of data.
Functionalities of SPSS program are listed below:
- Breadth of functionality: SPSS has the capability of handling flexible data and excellent data manipulation utilities. SPSS can read spread sheet files and can read data in any format such as numeric, dollar, date, alphanumeric, etc.
- State of the Art: SPSS can be considered as the state of the art of product. For example, SPSS Release 6.0 is the leading statistical software which includes the graphical module at base with excellent interface.
- User Friendliness: SPSS is easy to use and easy to learn command language.
- Robustness and reliability: SPSS is not known for its reliability. SPSS may not identify the new bugs because bugs in the SPSS are corrected by applying the endless stream of patches and will break the other software installed on the system.
In order to help in decision making, SPSS is used to interpret the row data into feasible information. SPSS is a program that is used for statistical analysis in social Science, especially in research and education. SPSS is used by market researchers, governments, data miners, survey organizations, etc. The main purpose of SPSS is to permit researchers to analyze data without using statistical analysis skills.
Various benefits of SPSS are listed below:
- Offers vast range of options: SPSS offers numerous methods, charts and graphs. It also involves the cleaning and better screening option for the analysis.
- Better output organization: it organized the output in a manner way as it kept the output separate from the data.
- Effective data management: it provides the excellent data management and reduces the manual work of the user.
IBM has released a wide range of SPSS products:
- IBM SPSS Statistics: it supports the statistical analysis of data that allows for comprehensive preparation and access. It also addresses the entire analytical process for better performance and decision making. Entire analytical process includes planning, collection of data, analysis and reporting. Some of the existing versions of this product are as follows:
- IBM SPSS Categories
- IBM SPSS Amos
- IBM SPSS Data Preparation
- IBM SPSS Decision Trees
- IBM SPSS Regression
- IBM SPSS Neural Networks
- IBM SPSS Modeler: it refers to a predictive analysis platform that gives lead to the predictive intelligence in decisions which is made by systems, groups, individuals and the enterprise. Some of the latest versions of this product are as follows:
- IBM SPSS Modeler Premium
- IBM SPSS Modeler on Cloud
- IBM SPSS Modeler Professional
- IBM SPSS Modeler Server
- IBM SPSS Modeler for Linux on System Z
- IBM SPSS Analytic Server: to improve the decisions with IBM SPSS Modeler, it allows the IBM predictive analytics platform to use data from Hadoop distributions.
- IBM SPSS deployment and collaboration services: it securely enables you to manage the automate processes and analytical assets.
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- Benefits of SPSS are - Perform data management operation effectively
- More options are available for statistical analysis
- Generate better output
SPSS Homework help for :
- Elements of Research Design, Hypotheses and Theories, Independent, Intervening, Dependent Variables, Data, Variables, Values, Overview of Qualitative Research, Qualitative versus Quantitative Analysis, The Comparative Method, Congruence Procedures, Process-tracing, Crucial Cases, The Basics of Data Analysis, Descriptive v. Inferential Data Analysis, Measuring Variables (validity, reliability, replicability), Types of Variables (nominal, ordinal, interval), Common Terms (dataset, population sample, parameter, statistic), Misuses of Data (examples), Univariate (Descriptive) Statistics, Sample Size (N), Range, Frequency Distributions, Histograms, Other Charts, Measures of Central Tendency and Dispersion
- ¾ Means, medians, modes, ¾ Variance, standard deviation, Introduction to SPSS for Windows, Starting an SPSS Session, Creating a New Dataset, Using an Existing Dataset, Manipulating and Merging Datasets, Importing and Exporting Data Printing Datasets Descriptive Statistics in SPSS (mean, standard deviation, variance, range, frequencies) Manipulating Data in SPSS Recoding and Transforming Variables, Graphs and Charts, ¾ Scatterplots, ¾ Histograms, ¾ Box Plots and Other Charts, ¾ Cross-tabulations, Printing and Saving Output, Probabilities and Sampling, Binomial and Normal Random Variables, The Meaning of “Normal”, Z-scores, Using the Normal Table, Other distributions, Methods of Sampling
- ¾ Systematic Sampling, ¾ Random Sampling, Sampling Error, Hypothesis-testing, Confidence Intervals, Estimation Procedures, Null and Alternative Hypotheses, One and Two-Tailed Tests, Bivariate Correlation and Regression, Introduction to Bivariate Analysis, Covariance and the Correlation Coefficient, Graphing the Function, Regression and the Method of Ordinary Least Squares (OLS), Interpreting Regression Statistics (Beta Coefficient and R-Squared), Bivariate Regression Analysis in SPSS, Correlations, Bivariate Regression Analysis, Interpreting the Statistics, , Multivariate Regression Analysis in SPSS, Introduction to Multivariate Regression Analysis, When to use Multivariate Regression, Control Variables, Goodness of Fit (R-squared statistic)
NVIVO, NUDIST, and ATLAS ,LISREL, AMOS, and EQS, structural equation modeling (SEM) ,SAS, STATA, R, MPlus, SPlus, Minitab, STATISTICA ,MATLAB for simulations ,bootstrapping, neural nets ,MATHEMATICA, MAPLE
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